A hybrid genetic algorithm and grey wolf optimizer technique for faster global peak detection in PV system under partial shading

In the present scenario, renewable energy is the fastest growing trend in the energy sector. Chief among the renewable power sources is solar photovoltaic (PV) technology. One of the major problems of solar PV systems is the occurrence of a partial shadow over the PV array, which generates multiple peaks in the power versus voltage (P-V) characteristics of the PV array. Under these conditions, conventional Maximum Power Point Tracking (MPPT) techniques tend to get stuck on local peaks and fail to track the Global Maximum Power Point (GMPP). In this paper, an improved hybrid optimization algorithm named as Genetic Algorithm-Grey Wolf Optimizer (GA-GWO) algorithm, which combines the abilities of the Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO) algorithms, has been proposed to track the global peak. The proposed system has been modeled and examined using MATLAB/Simulink software for various partial and uniform shading conditions. It has also been compared with previously existing GWO and GA algorithms. The proposed algorithm is found to offer faster and better tracking of GMPP than both GWO and GA algorithms.